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Validation of control of course and depth of biomimetic underwater vehicle with two side and two tail fins

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Underwater vehicles that mimic the movements of marine animals have gained popularity in recent years due to their potential for increased manoeuvrability and efficiency. However, optimising the control systems of these vehicles for various underwater conditions remains a significant challenge due to their unconventional propulsion systems. This study is focused on the control of the course and depth of the mini CyberSeal, i.e. a biomimetic underwater vehicle equipped with two side and two tail fins. A key contribution of this research is the mathematical model of the hydrodynamic forces generated by the biomimetic propulsion system. These forces have been measured experimentally at various oscillation frequencies and fin deflections, accurately representing the propulsion dynamics for simulation and control design. Two different control strategies were implemented to achieve effective manoeuvrability: a proportional, integral, and derivative (PID) controller and a sliding mode controller (SMC). These controllers were designed to regulate the vehicle’s depth and course, and their parameters were selected based on simulation results. The control strategies were verified by a comparative analysis of simulation and experimental results. The results indicate that the proposed mathematical model is sufficient to tune the controller based on the simulation, and the experimental data confirm effective depth and course control.
Rocznik
Tom
Strony
24--32
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Bibliografia
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  • 6. Hożyń S. An automated system for analysing swim-fins efficiency. NAŠE MORE : Znanstveni Časopis Za More i Pomorstvo 2020. https://doi.org/10.17818/NM/2020/3.9.
  • 7. Szymak P, Przybylski M. Thrust measurement of biomimetic underwater vehicle with undulating propulsion. Maritime Technical Journal 2018. https://doi.org/10.2478/sjpna-2018-0014.
  • 8. Morawski M, Malec M. Depth control for biomimetic and hybrid unmanned underwater vehicles. TechTrans 2021. https://doi.org/10.37705/TechTrans/e2021024.
  • 9. Grządziela A, Hożyń S. Simulation tests of a drive shaft and propeller control subsystem for a fast boat. Polish Maritime Research 2024. https://doi.org/10.2478/pomr-2024-0018.
  • 10. Dong Z, Li J, Liu W, Zhang H, Qi S, Zhang Z. Adptive heading control of underactuated unmanned surface vehicle based on improved backpropagation neural network. Polish Maritime Research 2023. https://doi.org/10.2478/pomr-2023-0006.
  • 11. Wang M, Wang K, Zhao Q, Zheng X, Gao H, Yu J. LQR control and optimization for trajectory tracking of biomimetic robotic fish based on Unreal Engine. Biomimetics 2023. https://doi.org/10.3390/biomimetics8020236.
  • 12. Śmierzchalski R, Kapczyński M. Autonomous control of the underwater remotely operated vehicle in collision situation with stationary obstacle. Polish Maritime Research 2022. https://doi.org/10.2478/pomr-2022-0043.
  • 13. Algarin-Pinto JA, Garza-Castanon LE, Vargas-Martinez A, Minchala-Avila LI, Payeur P. Intelligent motion control to enhance the swimming performance of a biomimetic underwater vehicle using reinforcement learning approach. IEEE Access 2025. https://doi.org/10.1109/ACCESS.2025.3544482.
  • 14. Mat-Noh M, Mohd-Mokhtar R, Arshad MR, Zain ZM, Khan Q. Review of sliding mode control application in autonomous underwater vehicles. Indian Journal of Geo-Marine Sciences 2019. https://nopr.niscpr.res.in/handle/123456789/48867.
  • 15. Guo X, Su S, Zuo Z. Simulation model validation of autonomous unmanned vehicle based on gray box identification and Bayesian statistics. Proceedings of the 5th International Conference on Communication and Information Processing, ACM, 2019. https://doi.org/10.1145/3369985.3370037.
  • 16. Szymak P, Praczyk T, Pietrukaniec L, Hożyń S. Laboratory stand for research on mini CyberSeal. Measurement Automation Monitoring 2017. https://bibliotekanauki.pl/articles/114481.
  • 17. Wang Y, Wang J, Yu L, Kong S, Yu J. Toward the intelligent, safe exploration of a biomimetic underwater robot: Modeling, planning, and control. Biomimetics 2024. https://doi.org/10.3390/biomimetics9030126.
  • 18. Przybylski M. Mathematical model of biomimetic underwater vehicle. ECMS 2019 Proceedings, ECMS, 2019. https://doi.org/10.7148/2019-0343.
  • 19. Aguzzi J, Costa C, Calisti M, Funari V, Stefanni S, Danovaro R, et al. Research trends and future perspectives in marine biomimicking robotics. Sensors 2021. https://doi.org/10.3390/s21113778.
  • 20. Fossen TI. Handbook of marine craft hydrodynamics and motion control. Wiley; 2021.
  • 21. Adeyiga JA, Sotonwa KA, Adenibuyan MT. Comparison of genetic algorithm and particle swarm optimization techniques in intelligent parking system. J Adv Mater Sci Eng 2022. https://doi.org/10.33425/2771-666X.1011.
  • 22. Przybylski M. Selection of the depth controller for the biomimetic underwater vehicle. Electronics 2023. https://doi.org/10.3390/electronics12061469.
  • 23. Hożyń S, Żak B. Stereo vision system for vision-based control of inspection-class ROVs. Remote Sensing 2021. https://doi.org/10.3390/rs13245075.
  • 24. Besbes M, Zolghadri M, Costa Affonso R, Masmoudi F, Haddar M. Performance comparison of particle swarm optimization and genetic algorithm combined with A*search for solving facility layout problem. JID 2022. https://doi.org/10.3233/JID-210024.
  • 25. Meng R, Sun A, Wu Z, Du X, Meng Y. 3D smooth path planning of AUV based on improved ant colony optimization considering heading switching pressure. Sci Rep 2023. https://doi.org/10.1038/s41598-023-39346-5.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ec718958-2753-4cc3-9e1e-23db38530611
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